doc. Ing.

Jakub Šimko

PhD.

External Cooperator


simkoj@fit.vut.cz
236028/BUT personal ID

Publication Results

  • 2026

    PECHER, B.; ČEGIŇ, J.; BELANEC, R.; SRBA, I.; ŠIMKO, J.; BIELIKOVÁ, M. Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification. Findings of the Association for Computational Linguistics: EACL 2026. Morocco: Association for Computational Linguistics, 2026. p. 2840-2857. ISBN: 979-8-89176-386-9. Detail

  • 2025

    ANIKINA, T.; ČEGIŇ, J.; ŠIMKO, J.; OSTERMANN, S. A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages. Suzhou, China: Association for Computational Linguistics, 2025. p. 8293-8314. ISBN: 979-8-89176-332-6. Detail

    ČEGIŇ, J.; PECHER, B.; ŠIMKO, J.; SRBA, I.; BIELIKOVÁ, M.; BRUSILOVSKY, P. Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation. Suzhou, China: Association for Computational Linguistics, 2025. p. 5533-5550. ISBN: 979-8-89176-335-7. Detail

    ČEGIŇ, J.; ŠIMKO, J. LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Albuquerque, New Mexico: Association for Computational Linguistics, 2025. p. 10476-10496. ISBN: 979-8-8917-6189-6. Detail

  • 2024

    ČEGIŇ, J.; PECHER, B.; ŠIMKO, J.; SRBA, I.; BIELIKOVÁ, M. Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Bangkok: Association for Computational Linguistics, 2024. p. 13148-13171. ISBN: 979-8-8917-6094-3. Detail

    PECHER, B.; ČEGIŇ, J.; BELANEC, R.; SRBA, I.; ŠIMKO, J.; BIELIKOVÁ, M. Fighting Randomness With Randomness: Mitigating Optimisation Instability of Fine-Tuning Using Ensemble and Noise Regularisation. Findings of the Association for Computational Linguistics: EMNLP 2024. Miami: Association for Computational Linguistics, 2024. p. 11005-11044. ISBN: 979-8-8917-6168-1. Detail

  • 2023

    ČEGIŇ, J.; ŠIMKO, J. ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Singapur: Association for Computational Linguistics, 2023. p. 1889-1905. ISBN: 979-8-8917-6060-8. Detail

    SRBA, I.; MÓRO, R.; TOMLEIN, M.; PECHER, B.; ŠIMKO, J.; ŠTEFANCOVÁ, E.; KOMPAN, M.; HRČKOVÁ, A.; PODROUŽEK, J.; GAVORNÍK, A.; BIELIKOVÁ, M. Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles. ACM transactions on recommender systems, 2023, vol. 1, no. 1, p. 1-33. ISSN: 2770-6699. Detail

  • 2022

    SRBA, I.; PECHER, B.; TOMLEIN, M.; MÓRO, R.; ŠTEFANCOVÁ, E.; ŠIMKO, J.; BIELIKOVÁ, M. Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Madrid: Association for Computing Machinery, 2022. p. 2949-2959. ISBN: 978-1-4503-8732-3. Detail

    TOMLEIN, M.; PECHER, B.; ŠIMKO, J.; SRBA, I.; MÓRO, R.; ŠTEFANCOVÁ, E.; KOMPAN, M.; HRČKOVÁ, A.; PODROUŽEK, J.; BIELIKOVÁ, M. Black-box Audit of YouTube's Video Recommendation: Investigation of Misinformation Filter Bubble Dynamics. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Sister Conferences Best Papers. Vienna: International Joint Conferences on Artificial Intelligence, 2022. p. 5349-5353. ISBN: 978-1-956792-00-3. Detail

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